ZSL mainly studies how to use the common attribute information of the training set and the test set to classify the test set when the data categories are mutually exclusive and there is no intersection, it can overcome a series of problems caused by missing samples or difficulty in collecting, which also provides ideas for other fields when facing sample problems[1]}, [2]}, [3]}. Zhu et al. proposed a zero-shot target detection architecture ZS-YOLO[4]}, which provides a semantic description of the target and corrects the confidence loss and part of the backbone network in the YOLOV2 framework to the rules required by the ZSL task, thus realizing the detection of unseen targets, and proved the rationality and effectiveness of its method through analysis and experiments. In view of the difficulty and cost of data collection in industrial faults, Feng et al. proposes an attribute transfer method based on fault descriptions through the ZSL idea [5]}, the detection of unknown faults is realized by describing the causes and conditions of each fault, and using these descriptions as auxiliary knowledge sources.
